Current Issue : October - December Volume : 2013 Issue Number : 4 Articles : 7 Articles
Background. Oligonucleotide microarrays allow for high-throughput gene expression profiling assays. The technology relies on\r\nthe fundamental assumption that observed hybridization signal intensities (HSIs) for each intended target, on average, correlate\r\nwith their target�s true concentration in the sample. However, systematic, nonbiological variation fromseveral sources undermines\r\nthis hypothesis. Background hybridization signal has been previously identified as one such important source, one manifestation\r\nof which appears in the form of spatial autocorrelation. Results. We propose an algorithm, pyn, for the elimination of spatial\r\nautocorrelation in HSIs, exploiting the duality of desirable mutual information shared by probes in a common probe set and\r\nundesirable mutual information shared by spatially proximate probes. We show that this correction procedure reduces spatial\r\nautocorrelation in HSIs; increases HSI reproducibility across replicate arrays; increases differentially expressed gene detection\r\npower; and performs better than previously published methods. Conclusions.The proposed algorithm increases both precision and\r\naccuracy, while requiring virtually no changes to users� current analysis pipelines: the correction consists merely of a transformation\r\nof raw HSIs (e.g., CEL files for Affymetrix arrays). A free, open-source implementation is provided as an R package, compatible\r\nwith standard Bioconductor tools.The approach may also be tailored to other platform types and other sources of bias....
Obtaining unique oligos from an EST database is a problem of great importance in bioinformatics, particularly in the discovery of\r\nnew genes and the mapping of the human genome.Many algorithms have been developed to find unique oligos, many of which are\r\nmuch less time consuming than the traditional brute force approach. An algorithm was presented by Zheng et al. (2004) which finds\r\nthe solution of the unique oligos search problem efficiently.We implement this algorithm as well as several new algorithms based\r\non some theorems included in this paper.We demonstrate how, with these new algorithms, we can obtain unique oligosmuch faster\r\nthan with previous ones.We parallelize these new algorithms to further improve the time of finding unique oligos. All algorithms\r\nare run on ESTs obtained from a Barley EST database....
Common microarray and next-generation sequencing data analysis concentrate on tumor subtype classification,marker detection,\r\nand transcriptional regulation discovery during biological processes by exploring the correlated gene expression patterns and their\r\nshared functions. Genetic regulatory network (GRN) based approaches have been employed in many large studies in order to\r\nscrutinize for dysregulation and potential treatment controls. In addition to gene regulation and network construction, the concept\r\nof the network modulator that has significant systemic impact has been proposed, and detection algorithms have been developed\r\nin past years. Here we provide a unified mathematic description of these methods, followed with a brief survey of these modulator\r\nidentification algorithms. As an early attempt to extend the concept to new RNA regulation mechanism, competitive endogenous\r\nRNA (ceRNA), into amodulator framework, we provide two applications to illustrate the network construction,modulation effect,\r\nand the preliminary finding from these networks. Those methods we surveyed and developed are used to dissect the regulated\r\nnetwork under different modulators. Not limit to these, the concept of ââ?¬Å?modulationââ?¬Â can adapt to various biological mechanisms\r\nto discover the novel gene regulation mechanisms....
One important problem in translational genomics is the identification of reliable and reproducible markers that can be used to\r\ndiscriminate between different classes of a complex disease, such as cancer.The typical small sample setting makes the prediction\r\nof such markers very challenging, and various approaches have been proposed to address this problem. For example, it has been\r\nshown that pathway markers, which aggregate the gene activities in the same pathway, tend to be more robust than gene markers.\r\nFurthermore, the use of gene expression ranking has been demonstrated to be robust to batch effects and that it can lead to more\r\ninterpretable results. In this paper, we propose an enhanced pathway activity inference method that uses gene ranking to predict the\r\npathway activity in a probabilistic manner.Themain focus of this work is on identifying robust pathwaymarkers that can ultimately\r\nlead to robust classifiers with reproducible performance across datasets. Simulation results based onmultiple breast cancer datasets\r\nshow that the proposed inference method identifies better pathway markers that can predict breast cancer metastasis with higher\r\naccuracy. Moreover, the identified pathway markers can lead to better classifiers with more consistent classification performance\r\nacross independent datasets....
This paper proposes a novel algorithm for inferring gene regulatory networks whichmakes use of cubature Kalman filter (CKF) and\r\nKalman filter (KF) techniques in conjunction with compressed sensingmethods. The gene network is described using a state-space\r\nmodel. A nonlinear model for the evolution of gene expression is considered, while the gene expression data is assumed to follow a\r\nlinear Gaussian model. The hidden states are estimated using CKF.The system parameters aremodeled as a Gauss-Markov process\r\nand are estimated using compressed sensing-based KF.These parameters provide insight into the regulatory relations among the\r\ngenes. The Cram�´er-Rao lower bound of the parameter estimates is calculated for the system model and used as a benchmark to\r\nassess the estimation accuracy. The proposed algorithm is evaluated rigorously using synthetic data in different scenarios which\r\ninclude different number of genes and varying number of sample points. In addition, the algorithm is tested on the DREAM4 in\r\nsilico data sets as well as the in vivo data sets from IRMA network. The proposed algorithm shows superior performance in terms\r\nof accuracy, robustness, and scalability....
Solving some mathematical problems such as NP-complete problems by conventional silicon-based computers is problematic and\r\ntakes so long time. DNA computing is an alternative method of computing which uses DNA molecules for computing purposes.\r\nDNA computers have massive degrees of parallel processing capability. The massive parallel processing characteristic of DNA\r\ncomputers is of particular interest in solving NP-complete and hard combinatorial problems. NP-complete problems such as\r\nknapsack problem and other hard combinatorial problems can be easily solved by DNA computers in a very short period of time\r\ncomparing to conventional silicon-based computers. Sticker-based DNA computing is one of the methods of DNA computing. In\r\nthis paper, the sticker based DNA computing was used for solving the 0/1 knapsack problem. At first, a biomolecular solution space\r\nwas constructed by using appropriate DNAmemory complexes. Then, by the application of a sticker-based parallel algorithm using\r\nbiological operations, knapsack problem was resolved in polynomial time....
Time-course expression profiles and methods for spectrum analysis have been applied for detecting transcriptional periodicities,\r\nwhich are valuable patterns to unravel genes associated with cell cycle and circadian rhythm regulation. However, most of the\r\nproposed methods suffer fromrestrictions and large false positives to a certain extent.Additionally, in some experiments, arbitrarily\r\nirregular sampling times as well as the presence of high noise and small sample sizes make accurate detection a challenging task.\r\nA novel scheme for detecting periodicities in time-course expression data is proposed, in which a real-valued iterative adaptive\r\napproach (RIAA), originally proposed for signal processing, is applied for periodogram estimation. The inferred spectrum is then\r\nanalyzed using Fisher�s hypothesis test. With a proper ??-value threshold, periodic genes can be detected. A periodic signal, two\r\nnonperiodic signals, and four sampling strategies were considered in the simulations, including both bursts and drops. In addition,\r\ntwo yeast real datasetswere applied for validation.The simulations and real data analysis reveal that RIAAcan performcompetitively\r\nwith the existing algorithms. The advantage of RIAA is manifested when the expression data are highly irregularly sampled, and\r\nwhen the number of cycles covered by the sampling time points is very reduced....
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